Understanding Idiopathic Pulmonary Fibrosis with CT Scans and Genetics
This observational study is looking at new ways to understand Idiopathic Pulmonary Fibrosis (IPF), a serious lung disease. Researchers are using detailed CT scans (High Resolution Computed Tomography or HRCT) of the chest and blood samples to find patterns that might help predict how the disease will progress and how well treatments might work. They want to see if these patterns, called radiomic biomarkers, and certain genetic markers are linked to how severe IPF is and how it changes over 12 months. You might be able to join if you are 40 to 101 years old and have been diagnosed with IPF. The study is currently unclear on its recruitment status.
- Study design
- This is an observational study planning to enroll 160 participants. It is not a treatment study but rather aims to gather information.
- What's involved
- You would have two HRCT scans, one historical and one at 12 months, and a blood draw. You can choose to share existing CTs or have a new research CT.
- Compensation
- Not stated in the trial record.
- Follow-up
- Participants will be followed for 12 months to assess changes in disease severity and outcomes.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
The Role of Quantitative CT and Radiomic Biomarkers for Precision Medicine in Pulmonary Fibrosis
At a glance
Conditions
NCT06323876
Where you'd take part
This study runs at 1 site. They're the same protocol — you choose where, and that choice sets who your contact draft is addressed to.
University of Virginia
Charlottesville, Virginiastudy coordinator listed
Recruiting
Sites open and close at different times, so the status above is per site — it can differ from the study's overall status.
Study leadership
- Noth Imre, MD · PRINCIPAL_INVESTIGATOR · Division of Pulmonary and Critical Care
- John Kim · PRINCIPAL_INVESTIGATOR · Division of Pulmonary and Critical Care
Who to contact
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Exclusion
What this trial measures
- Derivation of DTA in IPF only cases from the PFF-PR and its associations with disease severity and outcomes.12 months
Driven texture analysis (DTA) is a machine learning method capable of automatic detection and quantification of lung fibrosis on HRCT. It is trained to discriminate fibrosis using radiologist-identified image regions demonstrating normal lung parenchyma and usual interstitial pneumonia patterns. Changes in Forced Vital Capacity (FVC) measured in liters, reflect increased elastic recoil caused by fibrosis. We will use linear-mixed effects models with random intercept to examine associations of repeated DTA-fibrosis scores with repeated percent predicted FVC measurements over time (12 months minimum). This approach will provide a more precise estimate, power, and account for baseline FVC at an individual level which has implications of how rapid a decline we anticipate. This is the most common approach to examine longitudinal changes of FVC in IPF studies. FVC decline greater than 10% has been shown to be prognostic of worse survival and is a common endpoint in IPF clinical trials.
- Determine whether known IPF-risk genetic variants are associated with DTA score.12 months
This is a cross-sectional analysis to determine whether genetic variants that confer higher risk of disease and progression are associated with higher DTA scores from CT.
- Identify novel genetic variants that associate with DTA score progression.12 months
Determine novel genetic variants that indicate higher risk of disease progression and are associated with higher DTA scores.
- Determine if DTA or any constituent radiomic features correlate with select plasma proteins.12 months
MMP-7, CA-125, YKL, OPN, CCL18 are plasma proteins that have been shown to be associated with risk and prognosis in IPF.
- Determine if DTA or any of constituent radiomic features correlate with transcriptomic12 months
We have previously published a transcriptomic classifier that is predictive of FVC decline in IPF.
- Determine the best combination of markers (DTA, proteins and transcriptome) for machine learning algorithms for AUC evaluation of ROCs on all 3 cohorts.12 months
12-month FVC decline is a validated marker of disease progression in IPF as it's predictive of worse mortality. Receiver operating characteristic curve (ROC) is an analytical method, represented as a graph, that is used to evaluate the performance of a binary diagnostic classification method. The diagnostic test results need to be classified into one of the clearly defined dichotomous categories, such as the presence or absence of a disease. Area under the ROC curve (AUC) measures the entire two-dimensional area underneath the entire ROC curve.